CUBRC's Center for Transportation Injury Research

Initially, the mission of the CenTIR was to perform research leading to a reduction in motor vehicle crash deaths, crash injuries and the social and financial costs associated with these events. Several research projects were therefore directed at acquiring a better understanding of the causes of crash injury, which could then drive recommendations for in-vehicle safety improvements, educational programs to change unsafe behavior, etc. Accepting that some serious injury crashes will inevitably occur, other research efforts focused on improving post-crash notification and emergency response to the scene, including assessments of trauma system access and development of advanced information tools and technologies to improve real-time triage, transport and treatment decision-making.

Recently, the CenTIR’s mission was expanded to include research to develop the ‘next generation’ of emergency response technologies. The intent is to exploit anticipated new technologies (e.g., cyber-physical systems), which could greatly improve the capabilities of emergency responders, whether they are responding to a single motor vehicle crash or to a large scale disaster. The goal is to make a ‘generational leap’ in emergency response and rescue capabilities – a leap guided by a vision for the future as it might appear in 20 years under a (post-IntelliDrive), fully Integrated Active Transportation System (IATS). By performing the advanced research required to realize this vision now, emergency responders will be poised and ready to take full advantage of the upcoming cyber technology revolution.

The project suggests a bottom-up travel behavior driven approach which obtains trends in individual travel behavior first and use such information to enhance longitudinal origin-destination demand monitoring.

Extending the work that was completed for year one funding related to “Developing Highway Safety Performance Metrics in an Advanced Connected Vehicle Environment Utilizing Near-Crash Events from the SHRP 2 Naturalistic Driving Study.”

Integrating machine learning, big data, sensor networks, and agent-based transportation modeling to prototype an algorithm that combines the power of a model-driven approach with the power of big data.

Creating a quality-aware crowdsourced road sensing system that integrates sensory data from multiple vehicles while placing more weight on the vehicles that provide high quality data to significantly improve integration accuracy.

The project investigates how real-time conditions interact to affect driver safety performance changes. From that understanding, practitioners and drivers can make more informed decisions to reduce the likelihood of a crash.